Wavelet Estimation of a Density in a GARCH-type Model
نویسندگان
چکیده
منابع مشابه
Wavelet estimation of a density in a GARCH-type model
We consider the GARCH-type model: S = σZ, where σ and Z are independent random variables. The density of σ is unknown whereas the one of Z is known. We want to estimate the density of σ from n observations of S under some dependence assumption (the exponentially strongly mixing dependence). Adopting the wavelet methodology, we construct a nonadaptive estimator based on projections and an adapti...
متن کاملWavelet Linear Density Estimation for a GARCH Model under Various Dependence Structures
We consider n observations from the GARCH-type model: S = σ2Z, where σ2 and Z are independent random variables. We develop a new wavelet linear estimator of the unknown density of σ2 under four different dependence structures: the strong mixing case, the β- mixing case, the pairwise positive quadrant case and the ρ-mixing case. Its asymptotic mean integrated squared error properties are ...
متن کاملWavelet Density Estimation and Statistical Evidences Role for a GARCH Model in the Weighted Distribution
We consider n observations from the GARCH-type model: Z = UY, where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper bound of the associated mean integrated square error. We also make use of the measure of expected true evidence, so as to determine when model leads to a crisis and causes data to be l...
متن کاملWavelet Based Estimation of the Derivatives of a Density for m-Dependent Random Variables
Here, we propose a method of estimation of the derivatives of probability density based wavelets methods for a sequence of m−dependent random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for the such estimators.
متن کاملWavelet-based density estimation in a heteroscedastic convolution model
We consider a heteroscedastic convolution density model under the “ordinary smooth assumption”. We introduce a new adaptive wavelet estimator based on term-by-term hard thresholding rule. Its asymptotic properties are explored via the minimax approach under the mean integrated squared error over Besov balls. We prove that our estimator attains near optimal rates of convergence (lower bounds are...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2013
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2011.575516